A blood flow field estimation apparatus is provided, including an estimation unit that uses a learned model obtained in advance by performing machine learning to learn a relationship between organ tissue three-dimensional structure data including image data of a plurality of organ cross-sectional images serving as cross-sectional images of an organ and having each pixel provided with two or more bit depths and image position information serving as information indicating a position of an image reflected on each of the organ cross-sectional images in the organ, and a blood flow field in the organ, and estimates the blood flow field in the organ of an estimation target, based on the organ tissue three-dimensional structure data of the organ of the estimation target, and an output unit that outputs an estimation result of the estimation unit.

A method of training an image classification neural network comprises: presenting a first plurality of images to an observer as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of a target by the observer in at least one image of the first plurality of images; training the image classification neural network to identify the target in the image, based on the identification of the neurophysiological event; and storing the trained image classification neural network in a computer-readable storage medium.

An information display method displays brain activity information calculated using a biological signal measured from a living body so as to be superimposed on a morphological image including a plurality of sectional images. The information display method includes: acquiring mask information representing an area belonging to a brain in the morphological image; and detecting one or more extremal value positions with spatial extremal values in the brain activity information based on the mask information. A sectional image corresponding to the one or more extremal value positions is acquired from the morphological image. A brain activity distribution corresponding to the sectional image is acquired. The brain activity distribution is displayed so as to be superimposed on the sectional image.

A method of quantitatively evaluating a cognitive impairment and its future change from a medical image of an individual's brain, the method comprising scanning the individual's brain with a scanning device so as to acquire at least one medical brain image; processing the medical brain image to obtain at least one feature of the image; using a pre-established prediction model to determine a condition of the cognitive impairment and predict its future change based on the at least one feature obtained.

The present disclosure provides an operating method of a dopamine transporter check system, and the operation method includes steps as follows. A scan image of a subject's brain is obtained from a scan machine, and the scan image is a three-dimensional image. The scan image is aligned to a standard brain space to obtain a standardized scan image. Intensity normalization is performed on the standardized scan image. The standardized scan image after the intensity normalization is converted into a two-dimensional image. A plurality of image data are got from at least one region of interest in the two-dimensional image, and the at least one region of interest includes a left caudate, a left putamen, a right caudate and a right putamen. A dopamine neuron loss degree measurement and evaluation model based on the image data is established through a transfer learning.

The invention relates to a medical data processing method for determining the position of a region of interest serving as a start condition for conducting diffusion image-based tracking of nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data.

Systems and methods are provided for segmenting tissue within a computed tomography (CT) scan of a region of interest into one of a plurality of tissue classes. A plurality of atlases are registered to the CT scan to produce a plurality of registered atlases. A context model representing respective likelihoods that each voxel of the CT scan is a member of each of the plurality of tissue classes is determined from the CT scan and a set of associated training data. A proper subset of the plurality of registered at lases is selected according to the context model and the registered atlases. The selected proper subset of registered atlases are fused to produce a combined segmentation.

An image processing device includes: an image data acquisition unit for acquiring SPECT image data of a brain; a brain-region ROI definition unit for defining a brain-region ROI in the SPECT image; a striatum ROI definition unit for defining a striatum ROI in the SPECT image; and a threshold determination unit for, based on counts in the SPECT image's background which is the brain-region ROI except the striatum ROI, determining a threshold for distinguishing ventricles and sulci in the SPECT image; a region distinction unit for distinguishing between a region whose number of counts is smaller than or equal to the threshold and a region whose number of counts is larger than the threshold.

The invention relates to a medical data processing method for determining the position of a nerve fiber based on a diffusion image-based tracking method of tracking nerve fibers. In one example, the method encompasses comparing a set of tracked nerve fibers to a model of nerve fibers contained in atlas data.

The invention relates to a computer-implemented medical data processing method for determining a mapping of medical image content into a reference system, the method comprising executing, on a processor of a computer, steps of: a) acquiring, at the processor, medical image data describing a digital medical image of an anatomical structure of a patient's body; b) acquiring, at the processor, image attribute data describing attribute information associated with the medical image data, the attribute information including an indication of an initial reference system in which spatial relationships of the digital medical image are defined; c) acquiring, at the processor, reference system transformation data describing a spatial relationship (REG) between the initial reference system and a second reference system which is different from the initial reference system; d) determining, by the processor and based on the medical image data and the reference system transformation data, transformed image data describing a representation of the digital medical image in the second reference system.